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Article

A Low-Cost System for Moisture Content Detection of Bagasse upon a Conveyor Belt with Multispectral Image and Various Machine Learning Methods

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Department of Agricultural Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2
Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
3
Applied Engineering for Important Crops of the North East Research Group, Department of Agricultural, Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
*
Authors to whom correspondence should be addressed.
Academic Editor: Andrea Pizzi
Processes 2021, 9(5), 777; https://doi.org/10.3390/pr9050777
Received: 31 March 2021 / Revised: 24 April 2021 / Accepted: 27 April 2021 / Published: 28 April 2021
This research aimed to propose an online system based on multispectral images for the real-time estimation of the moisture content (MC) of sugarcane bagasse. The system consisted of a conveyor belt, four halogen bulbs, and a multispectral camera. The MC models were developed using machine learning algorithms, i.e., multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), PCA-ANN, Gaussian process regression (GPR), PCA-GPR, random forest regression (RFR), and PCA-GPR. The models were developed using 150 samples (calibration set) meanwhile the remaining 50 samples were applied as a validation set. The comparison of all developed models showed that the PCA-RFR model achieved better detection with a higher accuracy of MC prediction. The PCA-RFR model showed the best results which were a coefficient of determination of prediction (r2) of 0.72, root mean square error of prediction (RMSEP) of 11.82 wt%, and a ratio of the standard error of prediction to standard deviation (RPD) of 1.85. The results show that this technique was very useful for MC rapid screening of the sugarcane bagasse. View Full-Text
Keywords: moisture content; sugarcane bagasse; multispectral reflectance imagery; real-time estimation moisture content; sugarcane bagasse; multispectral reflectance imagery; real-time estimation
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MDPI and ACS Style

Nakawajana, N.; Lerdwattanakitti, P.; Saechua, W.; Posom, J.; Saengprachatanarug, K.; Wongpichet, S. A Low-Cost System for Moisture Content Detection of Bagasse upon a Conveyor Belt with Multispectral Image and Various Machine Learning Methods. Processes 2021, 9, 777. https://doi.org/10.3390/pr9050777

AMA Style

Nakawajana N, Lerdwattanakitti P, Saechua W, Posom J, Saengprachatanarug K, Wongpichet S. A Low-Cost System for Moisture Content Detection of Bagasse upon a Conveyor Belt with Multispectral Image and Various Machine Learning Methods. Processes. 2021; 9(5):777. https://doi.org/10.3390/pr9050777

Chicago/Turabian Style

Nakawajana, Natrapee, Patchara Lerdwattanakitti, Wanphut Saechua, Jetsada Posom, Khwantri Saengprachatanarug, and Seree Wongpichet. 2021. "A Low-Cost System for Moisture Content Detection of Bagasse upon a Conveyor Belt with Multispectral Image and Various Machine Learning Methods" Processes 9, no. 5: 777. https://doi.org/10.3390/pr9050777

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